Overview

Dataset statistics

Number of variables24
Number of observations48761
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.6 MiB
Average record size in memory185.0 B

Variable types

Numeric17
Categorical7

Alerts

ID has a high cardinality: 1795 distinct values High cardinality
Name has a high cardinality: 288 distinct values High cardinality
Latitude has a high cardinality: 597 distinct values High cardinality
Longitude has a high cardinality: 1032 distinct values High cardinality
df_index is highly correlated with DateHigh correlation
Date is highly correlated with df_indexHigh correlation
Minimum Pressure is highly correlated with df_index and 1 other fieldsHigh correlation
Low Wind NE is highly correlated with Status and 3 other fieldsHigh correlation
Low Wind SE is highly correlated with Low Wind SW and 3 other fieldsHigh correlation
Low Wind SW is highly correlated with Low Wind SE and 1 other fieldsHigh correlation
Low Wind NW is highly correlated with Status and 4 other fieldsHigh correlation
Moderate Wind NE is highly correlated with Status and 6 other fieldsHigh correlation
Moderate Wind SE is highly correlated with Status and 6 other fieldsHigh correlation
Moderate Wind SW is highly correlated with Moderate Wind NE and 3 other fieldsHigh correlation
Moderate Wind NW is highly correlated with Moderate Wind NE and 3 other fieldsHigh correlation
High Wind NE is highly correlated with Maximum Wind and 2 other fieldsHigh correlation
High Wind SE is highly correlated with Status and 6 other fieldsHigh correlation
High Wind SW is highly correlated with Maximum Wind and 3 other fieldsHigh correlation
High Wind NW is highly correlated with High Wind NE and 1 other fieldsHigh correlation
Time is highly correlated with EventHigh correlation
Event is highly correlated with TimeHigh correlation
Status is highly correlated with Maximum Wind and 6 other fieldsHigh correlation
Maximum Wind is highly correlated with Status and 8 other fieldsHigh correlation
Category is highly correlated with Status and 2 other fieldsHigh correlation
df_index has unique values Unique
Time has 11871 (24.3%) zeros Zeros
Low Wind NE has 2084 (4.3%) zeros Zeros
Low Wind SE has 2247 (4.6%) zeros Zeros
Low Wind SW has 3060 (6.3%) zeros Zeros
Low Wind NW has 2623 (5.4%) zeros Zeros
Moderate Wind NE has 3762 (7.7%) zeros Zeros
Moderate Wind SE has 3927 (8.1%) zeros Zeros
Moderate Wind SW has 4261 (8.7%) zeros Zeros
Moderate Wind NW has 4095 (8.4%) zeros Zeros
High Wind NE has 4731 (9.7%) zeros Zeros
High Wind SE has 4767 (9.8%) zeros Zeros
High Wind SW has 4929 (10.1%) zeros Zeros
High Wind NW has 4890 (10.0%) zeros Zeros

Reproduction

Analysis started2022-09-29 18:52:55.086049
Analysis finished2022-09-29 18:53:19.408676
Duration24.32 seconds
Software versionpandas-profiling v3.3.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct48761
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24521.86807
Minimum0
Maximum49104
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size381.1 KiB
2022-09-29T11:53:19.468930image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2438
Q112190
median24383
Q336913
95-th percentile46666
Maximum49104
Range49104
Interquartile range (IQR)24723

Descriptive statistics

Standard deviation14219.54597
Coefficient of variation (CV)0.5798720527
Kurtosis-1.209892478
Mean24521.86807
Median Absolute Deviation (MAD)12362
Skewness0.005954603966
Sum1195710809
Variance202195487.7
MonotonicityStrictly increasing
2022-09-29T11:53:19.544787image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
< 0.1%
328291
 
< 0.1%
328201
 
< 0.1%
328211
 
< 0.1%
328221
 
< 0.1%
328231
 
< 0.1%
328241
 
< 0.1%
328251
 
< 0.1%
328261
 
< 0.1%
328271
 
< 0.1%
Other values (48751)48751
> 99.9%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
ValueCountFrequency (%)
491041
< 0.1%
491031
< 0.1%
491021
< 0.1%
491011
< 0.1%
491001
< 0.1%
490991
< 0.1%
490981
< 0.1%
490971
< 0.1%
490961
< 0.1%
490951
< 0.1%

ID
Categorical

HIGH CARDINALITY

Distinct1795
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Memory size381.1 KiB
AL031899
 
133
AL141971
 
118
AL201969
 
99
AL142012
 
96
AL041957
 
95
Other values (1790)
48220 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters390088
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique31 ?
Unique (%)0.1%

Sample

1st rowAL011851
2nd rowAL011851
3rd rowAL011851
4th rowAL011851
5th rowAL011851

Common Values

ValueCountFrequency (%)
AL031899133
 
0.3%
AL141971118
 
0.2%
AL20196999
 
0.2%
AL14201296
 
0.2%
AL04195795
 
0.2%
AL04192695
 
0.2%
AL09200494
 
0.2%
AL12200290
 
0.2%
AL03200087
 
0.2%
AL09189384
 
0.2%
Other values (1785)47770
98.0%

Length

2022-09-29T11:53:19.608741image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
al031899133
 
0.3%
al141971118
 
0.2%
al20196999
 
0.2%
al14201296
 
0.2%
al04195795
 
0.2%
al04192695
 
0.2%
al09200494
 
0.2%
al12200290
 
0.2%
al03200087
 
0.2%
al09189384
 
0.2%
Other values (1785)47770
98.0%

Most occurring characters

ValueCountFrequency (%)
169890
17.9%
058021
14.9%
A48761
12.5%
L48761
12.5%
946917
12.0%
823637
 
6.1%
222079
 
5.7%
515045
 
3.9%
314664
 
3.8%
614630
 
3.8%
Other values (2)27683
 
7.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number292566
75.0%
Uppercase Letter97522
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
169890
23.9%
058021
19.8%
946917
16.0%
823637
 
8.1%
222079
 
7.5%
515045
 
5.1%
314664
 
5.0%
614630
 
5.0%
714083
 
4.8%
413600
 
4.6%
Uppercase Letter
ValueCountFrequency (%)
A48761
50.0%
L48761
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common292566
75.0%
Latin97522
 
25.0%

Most frequent character per script

Common
ValueCountFrequency (%)
169890
23.9%
058021
19.8%
946917
16.0%
823637
 
8.1%
222079
 
7.5%
515045
 
5.1%
314664
 
5.0%
614630
 
5.0%
714083
 
4.8%
413600
 
4.6%
Latin
ValueCountFrequency (%)
A48761
50.0%
L48761
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII390088
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
169890
17.9%
058021
14.9%
A48761
12.5%
L48761
12.5%
946917
12.0%
823637
 
6.1%
222079
 
5.7%
515045
 
3.9%
314664
 
3.8%
614630
 
3.8%
Other values (2)27683
 
7.1%

Name
Categorical

HIGH CARDINALITY

Distinct288
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size381.1 KiB
UNNAMED
26222 
FRANCES
 
317
ARLENE
 
283
BERTHA
 
268
DENNIS
 
255
Other values (283)
21416 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters926459
Distinct characters28
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row UNNAMED
2nd row UNNAMED
3rd row UNNAMED
4th row UNNAMED
5th row UNNAMED

Common Values

ValueCountFrequency (%)
UNNAMED26222
53.8%
FRANCES317
 
0.7%
ARLENE283
 
0.6%
BERTHA268
 
0.5%
DENNIS255
 
0.5%
FLORENCE249
 
0.5%
HELENE240
 
0.5%
IRENE231
 
0.5%
EMILY225
 
0.5%
BONNIE217
 
0.4%
Other values (278)20254
41.5%

Length

2022-09-29T11:53:19.661093image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
unnamed26222
53.8%
frances317
 
0.7%
arlene283
 
0.6%
bertha268
 
0.5%
dennis255
 
0.5%
florence249
 
0.5%
helene240
 
0.5%
irene231
 
0.5%
emily225
 
0.5%
bonnie217
 
0.4%
Other values (278)20254
41.5%

Most occurring characters

ValueCountFrequency (%)
620753
67.0%
N62487
 
6.7%
E43529
 
4.7%
A41105
 
4.4%
D31466
 
3.4%
M27893
 
3.0%
U27814
 
3.0%
L10280
 
1.1%
I10147
 
1.1%
R9757
 
1.1%
Other values (18)41228
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Space Separator620753
67.0%
Uppercase Letter305679
33.0%
Dash Punctuation27
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N62487
20.4%
E43529
14.2%
A41105
13.4%
D31466
10.3%
M27893
9.1%
U27814
9.1%
L10280
 
3.4%
I10147
 
3.3%
R9757
 
3.2%
O6424
 
2.1%
Other values (16)34777
11.4%
Space Separator
ValueCountFrequency (%)
620753
100.0%
Dash Punctuation
ValueCountFrequency (%)
-27
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common620780
67.0%
Latin305679
33.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N62487
20.4%
E43529
14.2%
A41105
13.4%
D31466
10.3%
M27893
9.1%
U27814
9.1%
L10280
 
3.4%
I10147
 
3.3%
R9757
 
3.2%
O6424
 
2.1%
Other values (16)34777
11.4%
Common
ValueCountFrequency (%)
620753
> 99.9%
-27
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII926459
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
620753
67.0%
N62487
 
6.7%
E43529
 
4.7%
A41105
 
4.4%
D31466
 
3.4%
M27893
 
3.0%
U27814
 
3.0%
L10280
 
1.1%
I10147
 
1.1%
R9757
 
1.1%
Other values (18)41228
 
4.5%

Date
Real number (ℝ≥0)

HIGH CORRELATION

Distinct9931
Distinct (%)20.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19496680.27
Minimum18510625
Maximum20151113
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size381.1 KiB
2022-09-29T11:53:19.724439image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum18510625
5-th percentile18730919
Q119110831
median19560609
Q319890829
95-th percentile20100921
Maximum20151113
Range1640488
Interquartile range (IQR)779998

Descriptive statistics

Standard deviation447443.6529
Coefficient of variation (CV)0.02294973538
Kurtosis-1.044413772
Mean19496680.27
Median Absolute Deviation (MAD)370299
Skewness-0.3521183408
Sum9.506776265 × 1011
Variance2.002058225 × 1011
MonotonicityNot monotonic
2022-09-29T11:53:19.796912image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1971091224
 
< 0.1%
1971091123
 
< 0.1%
1971091321
 
< 0.1%
1995082719
 
< 0.1%
1971091419
 
< 0.1%
1971090818
 
< 0.1%
1971091018
 
< 0.1%
2008090317
 
< 0.1%
1900091317
 
< 0.1%
1998092517
 
< 0.1%
Other values (9921)48568
99.6%
ValueCountFrequency (%)
185106255
< 0.1%
185106264
< 0.1%
185106274
< 0.1%
185106281
 
< 0.1%
185107051
 
< 0.1%
185107101
 
< 0.1%
185108164
< 0.1%
185108174
< 0.1%
185108184
< 0.1%
185108194
< 0.1%
ValueCountFrequency (%)
201511133
< 0.1%
201511124
< 0.1%
201511114
< 0.1%
201511104
< 0.1%
201511094
< 0.1%
201511081
 
< 0.1%
201510151
 
< 0.1%
201510144
< 0.1%
201510134
< 0.1%
201510124
< 0.1%

Time
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct92
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean909.787248
Minimum0
Maximum2330
Zeros11871
Zeros (%)24.3%
Negative0
Negative (%)0.0%
Memory size381.1 KiB
2022-09-29T11:53:19.872454image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1600
median1200
Q31800
95-th percentile1800
Maximum2330
Range2330
Interquartile range (IQR)1200

Descriptive statistics

Standard deviation671.1764039
Coefficient of variation (CV)0.737728964
Kurtosis-1.343295871
Mean909.787248
Median Absolute Deviation (MAD)600
Skewness-0.01048888816
Sum44362136
Variance450477.7652
MonotonicityNot monotonic
2022-09-29T11:53:19.942948image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
120012185
25.0%
180012016
24.6%
60011882
24.4%
011871
24.3%
210063
 
0.1%
140042
 
0.1%
100041
 
0.1%
220040
 
0.1%
150038
 
0.1%
30038
 
0.1%
Other values (82)545
 
1.1%
ValueCountFrequency (%)
011871
24.3%
305
 
< 0.1%
451
 
< 0.1%
10027
 
0.1%
1301
 
< 0.1%
20034
 
0.1%
2301
 
< 0.1%
2451
 
< 0.1%
30038
 
0.1%
3152
 
< 0.1%
ValueCountFrequency (%)
23301
 
< 0.1%
23152
 
< 0.1%
230028
0.1%
22451
 
< 0.1%
22351
 
< 0.1%
22302
 
< 0.1%
220040
0.1%
21521
 
< 0.1%
21451
 
< 0.1%
21303
 
< 0.1%

Event
Categorical

HIGH CORRELATION

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size381.1 KiB
47798 
L
 
902
I
 
27
P
 
9
S
 
7
Other values (5)
 
18

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters97522
Distinct characters10
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row
2nd row
3rd row
4th row
5th row L

Common Values

ValueCountFrequency (%)
47798
98.0%
L902
 
1.8%
I27
 
0.1%
P9
 
< 0.1%
S7
 
< 0.1%
C5
 
< 0.1%
T5
 
< 0.1%
W4
 
< 0.1%
R3
 
< 0.1%
G1
 
< 0.1%

Length

2022-09-29T11:53:20.004149image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-29T11:53:20.288537image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
l902
93.7%
i27
 
2.8%
p9
 
0.9%
s7
 
0.7%
c5
 
0.5%
t5
 
0.5%
w4
 
0.4%
r3
 
0.3%
g1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
96559
99.0%
L902
 
0.9%
I27
 
< 0.1%
P9
 
< 0.1%
S7
 
< 0.1%
C5
 
< 0.1%
T5
 
< 0.1%
W4
 
< 0.1%
R3
 
< 0.1%
G1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Space Separator96559
99.0%
Uppercase Letter963
 
1.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
L902
93.7%
I27
 
2.8%
P9
 
0.9%
S7
 
0.7%
C5
 
0.5%
T5
 
0.5%
W4
 
0.4%
R3
 
0.3%
G1
 
0.1%
Space Separator
ValueCountFrequency (%)
96559
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common96559
99.0%
Latin963
 
1.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
L902
93.7%
I27
 
2.8%
P9
 
0.9%
S7
 
0.7%
C5
 
0.5%
T5
 
0.5%
W4
 
0.4%
R3
 
0.3%
G1
 
0.1%
Common
ValueCountFrequency (%)
96559
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII97522
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
96559
99.0%
L902
 
0.9%
I27
 
< 0.1%
P9
 
< 0.1%
S7
 
< 0.1%
C5
 
< 0.1%
T5
 
< 0.1%
W4
 
< 0.1%
R3
 
< 0.1%
G1
 
< 0.1%

Status
Categorical

HIGH CORRELATION

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size381.1 KiB
TS
17804 
HU
14531 
TD
9553 
EX
4798 
LO
 
1005
Other values (4)
 
1070

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters146283
Distinct characters13
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row HU
2nd row HU
3rd row HU
4th row HU
5th row HU

Common Values

ValueCountFrequency (%)
TS17804
36.5%
HU14531
29.8%
TD9553
19.6%
EX4798
 
9.8%
LO1005
 
2.1%
SS557
 
1.1%
SD293
 
0.6%
WV116
 
0.2%
DB104
 
0.2%

Length

2022-09-29T11:53:20.347224image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-29T11:53:20.410076image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
ts17804
36.5%
hu14531
29.8%
td9553
19.6%
ex4798
 
9.8%
lo1005
 
2.1%
ss557
 
1.1%
sd293
 
0.6%
wv116
 
0.2%
db104
 
0.2%

Most occurring characters

ValueCountFrequency (%)
48761
33.3%
T27357
18.7%
S19211
 
13.1%
H14531
 
9.9%
U14531
 
9.9%
D9950
 
6.8%
E4798
 
3.3%
X4798
 
3.3%
L1005
 
0.7%
O1005
 
0.7%
Other values (3)336
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter97522
66.7%
Space Separator48761
33.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T27357
28.1%
S19211
19.7%
H14531
14.9%
U14531
14.9%
D9950
 
10.2%
E4798
 
4.9%
X4798
 
4.9%
L1005
 
1.0%
O1005
 
1.0%
W116
 
0.1%
Other values (2)220
 
0.2%
Space Separator
ValueCountFrequency (%)
48761
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin97522
66.7%
Common48761
33.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
T27357
28.1%
S19211
19.7%
H14531
14.9%
U14531
14.9%
D9950
 
10.2%
E4798
 
4.9%
X4798
 
4.9%
L1005
 
1.0%
O1005
 
1.0%
W116
 
0.1%
Other values (2)220
 
0.2%
Common
ValueCountFrequency (%)
48761
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII146283
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
48761
33.3%
T27357
18.7%
S19211
 
13.1%
H14531
 
9.9%
U14531
 
9.9%
D9950
 
6.8%
E4798
 
3.3%
X4798
 
3.3%
L1005
 
0.7%
O1005
 
0.7%
Other values (3)336
 
0.2%

Latitude
Categorical

HIGH CARDINALITY

Distinct597
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size381.1 KiB
28.0N
 
294
18.0N
 
289
27.0N
 
285
20.0N
 
283
29.0N
 
281
Other values (592)
47329 

Length

Max length5
Median length5
Mean length4.995303624
Min length4

Characters and Unicode

Total characters243576
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique44 ?
Unique (%)0.1%

Sample

1st row28.0N
2nd row28.0N
3rd row28.0N
4th row28.1N
5th row28.2N

Common Values

ValueCountFrequency (%)
28.0N294
 
0.6%
18.0N289
 
0.6%
27.0N285
 
0.6%
20.0N283
 
0.6%
29.0N281
 
0.6%
14.0N269
 
0.6%
21.0N269
 
0.6%
30.0N265
 
0.5%
33.0N263
 
0.5%
26.0N260
 
0.5%
Other values (587)46003
94.3%

Length

2022-09-29T11:53:20.473437image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
28.0n294
 
0.6%
18.0n289
 
0.6%
27.0n285
 
0.6%
20.0n283
 
0.6%
29.0n281
 
0.6%
14.0n269
 
0.6%
21.0n269
 
0.6%
30.0n265
 
0.5%
33.0n263
 
0.5%
26.0n260
 
0.5%
Other values (587)46003
94.3%

Most occurring characters

ValueCountFrequency (%)
.48761
20.0%
N48761
20.0%
227373
11.2%
322001
9.0%
121880
9.0%
013093
 
5.4%
412684
 
5.2%
512352
 
5.1%
79676
 
4.0%
89421
 
3.9%
Other values (2)17574
 
7.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number146054
60.0%
Other Punctuation48761
 
20.0%
Uppercase Letter48761
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
227373
18.7%
322001
15.1%
121880
15.0%
013093
9.0%
412684
8.7%
512352
8.5%
79676
 
6.6%
89421
 
6.5%
69068
 
6.2%
98506
 
5.8%
Other Punctuation
ValueCountFrequency (%)
.48761
100.0%
Uppercase Letter
ValueCountFrequency (%)
N48761
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common194815
80.0%
Latin48761
 
20.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.48761
25.0%
227373
14.1%
322001
11.3%
121880
11.2%
013093
 
6.7%
412684
 
6.5%
512352
 
6.3%
79676
 
5.0%
89421
 
4.8%
69068
 
4.7%
Latin
ValueCountFrequency (%)
N48761
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII243576
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.48761
20.0%
N48761
20.0%
227373
11.2%
322001
9.0%
121880
9.0%
013093
 
5.4%
412684
 
5.2%
512352
 
5.1%
79676
 
4.0%
89421
 
3.9%
Other values (2)17574
 
7.2%

Longitude
Categorical

HIGH CARDINALITY

Distinct1032
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size381.1 KiB
65.0W
 
179
79.0W
 
176
84.0W
 
170
69.0W
 
168
66.0W
 
166
Other values (1027)
47902 

Length

Max length6
Median length5
Mean length5.000143557
Min length4

Characters and Unicode

Total characters243812
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique90 ?
Unique (%)0.2%

Sample

1st row94.8W
2nd row95.4W
3rd row96.0W
4th row96.5W
5th row96.8W

Common Values

ValueCountFrequency (%)
65.0W179
 
0.4%
79.0W176
 
0.4%
84.0W170
 
0.3%
69.0W168
 
0.3%
66.0W166
 
0.3%
70.0W166
 
0.3%
68.0W165
 
0.3%
78.0W164
 
0.3%
75.0W159
 
0.3%
61.0W158
 
0.3%
Other values (1022)47090
96.6%

Length

2022-09-29T11:53:20.532189image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
65.0w179
 
0.4%
79.0w176
 
0.4%
84.0w170
 
0.3%
69.0w168
 
0.3%
66.0w166
 
0.3%
70.0w166
 
0.3%
68.0w165
 
0.3%
78.0w164
 
0.3%
75.0w159
 
0.3%
61.0w158
 
0.3%
Other values (1022)47090
96.6%

Most occurring characters

ValueCountFrequency (%)
.48761
20.0%
W48713
20.0%
718510
 
7.6%
518502
 
7.6%
818065
 
7.4%
617731
 
7.3%
013792
 
5.7%
413736
 
5.6%
913030
 
5.3%
312433
 
5.1%
Other values (3)20539
8.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number146290
60.0%
Other Punctuation48761
 
20.0%
Uppercase Letter48761
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
718510
12.7%
518502
12.6%
818065
12.3%
617731
12.1%
013792
9.4%
413736
9.4%
913030
8.9%
312433
8.5%
211026
7.5%
19465
6.5%
Uppercase Letter
ValueCountFrequency (%)
W48713
99.9%
E48
 
0.1%
Other Punctuation
ValueCountFrequency (%)
.48761
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common195051
80.0%
Latin48761
 
20.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.48761
25.0%
718510
 
9.5%
518502
 
9.5%
818065
 
9.3%
617731
 
9.1%
013792
 
7.1%
413736
 
7.0%
913030
 
6.7%
312433
 
6.4%
211026
 
5.7%
Latin
ValueCountFrequency (%)
W48713
99.9%
E48
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII243812
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.48761
20.0%
W48713
20.0%
718510
 
7.6%
518502
 
7.6%
818065
 
7.4%
617731
 
7.3%
013792
 
5.7%
413736
 
5.6%
913030
 
5.3%
312433
 
5.1%
Other values (3)20539
8.4%

Maximum Wind
Real number (ℝ≥0)

HIGH CORRELATION

Distinct31
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.03843235
Minimum10
Maximum155
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size381.1 KiB
2022-09-29T11:53:20.592543image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile25
Q135
median50
Q370
95-th percentile100
Maximum155
Range145
Interquartile range (IQR)35

Descriptive statistics

Standard deviation24.72049827
Coefficient of variation (CV)0.4660865183
Kurtosis0.3025796613
Mean53.03843235
Median Absolute Deviation (MAD)20
Skewness0.8933806839
Sum2586207
Variance611.1030348
MonotonicityNot monotonic
2022-09-29T11:53:20.651561image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
305900
12.1%
404582
 
9.4%
354515
 
9.3%
254432
 
9.1%
504225
 
8.7%
453413
 
7.0%
603016
 
6.2%
702875
 
5.9%
552124
 
4.4%
652098
 
4.3%
Other values (21)11581
23.8%
ValueCountFrequency (%)
1061
 
0.1%
15193
 
0.4%
201237
 
2.5%
254432
9.1%
305900
12.1%
321
 
< 0.1%
354515
9.3%
404582
9.4%
453413
7.0%
504225
8.7%
ValueCountFrequency (%)
1559
 
< 0.1%
15026
 
0.1%
14530
 
0.1%
14064
 
0.1%
13556
 
0.1%
130112
 
0.2%
125173
 
0.4%
120300
0.6%
115318
0.7%
110508
1.0%

Minimum Pressure
Real number (ℝ)

HIGH CORRELATION

Distinct127
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-246.3261213
Minimum-999
Maximum1024
Zeros0
Zeros (%)0.0%
Negative30330
Negative (%)62.2%
Memory size381.1 KiB
2022-09-29T11:53:20.723404image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-999
5-th percentile-999
Q1-999
median-999
Q3990
95-th percentile1009
Maximum1024
Range2023
Interquartile range (IQR)1989

Descriptive statistics

Standard deviation965.6171608
Coefficient of variation (CV)-3.920076181
Kurtosis-1.745715099
Mean-246.3261213
Median Absolute Deviation (MAD)0
Skewness0.503734591
Sum-12011108
Variance932416.5012
MonotonicityNot monotonic
2022-09-29T11:53:20.796155image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-99930330
62.2%
1005883
 
1.8%
1008846
 
1.7%
1006808
 
1.7%
1009800
 
1.6%
1007754
 
1.5%
1000733
 
1.5%
1010679
 
1.4%
1004625
 
1.3%
1003596
 
1.2%
Other values (117)11707
 
24.0%
ValueCountFrequency (%)
-99930330
62.2%
8891
 
< 0.1%
8924
 
< 0.1%
8951
 
< 0.1%
8972
 
< 0.1%
9002
 
< 0.1%
9011
 
< 0.1%
9021
 
< 0.1%
9055
 
< 0.1%
9071
 
< 0.1%
ValueCountFrequency (%)
10242
 
< 0.1%
10234
 
< 0.1%
10227
 
< 0.1%
10214
 
< 0.1%
102012
 
< 0.1%
10196
 
< 0.1%
101811
 
< 0.1%
101723
 
< 0.1%
101651
0.1%
101584
0.2%

Low Wind NE
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct65
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-867.7755583
Minimum-999
Maximum710
Zeros2084
Zeros (%)4.3%
Negative42841
Negative (%)87.9%
Memory size381.1 KiB
2022-09-29T11:53:20.873009image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-999
5-th percentile-999
Q1-999
median-999
Q3-999
95-th percentile90
Maximum710
Range1709
Interquartile range (IQR)0

Descriptive statistics

Standard deviation354.3428382
Coefficient of variation (CV)-0.4083346608
Kurtosis3.634804926
Mean-867.7755583
Median Absolute Deviation (MAD)0
Skewness2.355394366
Sum-42313604
Variance125558.847
MonotonicityNot monotonic
2022-09-29T11:53:20.944519image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-99942841
87.9%
02084
 
4.3%
60364
 
0.7%
90304
 
0.6%
120285
 
0.6%
150281
 
0.6%
100276
 
0.6%
180233
 
0.5%
75205
 
0.4%
50148
 
0.3%
Other values (55)1740
 
3.6%
ValueCountFrequency (%)
-99942841
87.9%
02084
 
4.3%
105
 
< 0.1%
156
 
< 0.1%
2010
 
< 0.1%
2515
 
< 0.1%
30103
 
0.2%
3528
 
0.1%
40115
 
0.2%
45106
 
0.2%
ValueCountFrequency (%)
7101
 
< 0.1%
6201
 
< 0.1%
5801
 
< 0.1%
5404
 
< 0.1%
4805
 
< 0.1%
4602
 
< 0.1%
45023
< 0.1%
42019
< 0.1%
40010
< 0.1%
3901
 
< 0.1%

Low Wind SE
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct72
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-868.4239249
Minimum-999
Maximum600
Zeros2247
Zeros (%)4.6%
Negative42841
Negative (%)87.9%
Memory size381.1 KiB
2022-09-29T11:53:21.021280image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-999
5-th percentile-999
Q1-999
median-999
Q3-999
95-th percentile75
Maximum600
Range1599
Interquartile range (IQR)0

Descriptive statistics

Standard deviation352.5892203
Coefficient of variation (CV)-0.4060104866
Kurtosis3.634090083
Mean-868.4239249
Median Absolute Deviation (MAD)0
Skewness2.35532485
Sum-42345219
Variance124319.1583
MonotonicityNot monotonic
2022-09-29T11:53:21.093918image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-99942841
87.9%
02247
 
4.6%
60345
 
0.7%
120281
 
0.6%
150277
 
0.6%
90257
 
0.5%
100247
 
0.5%
75218
 
0.4%
30181
 
0.4%
180154
 
0.3%
Other values (62)1713
 
3.5%
ValueCountFrequency (%)
-99942841
87.9%
02247
 
4.6%
105
 
< 0.1%
155
 
< 0.1%
2041
 
0.1%
2516
 
< 0.1%
30181
 
0.4%
3520
 
< 0.1%
40132
 
0.3%
45112
 
0.2%
ValueCountFrequency (%)
6003
< 0.1%
5401
 
< 0.1%
5301
 
< 0.1%
5201
 
< 0.1%
5001
 
< 0.1%
4901
 
< 0.1%
4754
< 0.1%
4601
 
< 0.1%
4253
< 0.1%
4205
< 0.1%

Low Wind SW
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct63
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-871.8075716
Minimum-999
Maximum640
Zeros3060
Zeros (%)6.3%
Negative42841
Negative (%)87.9%
Memory size381.1 KiB
2022-09-29T11:53:21.171200image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-999
5-th percentile-999
Q1-999
median-999
Q3-999
95-th percentile40
Maximum640
Range1639
Interquartile range (IQR)0

Descriptive statistics

Standard deviation343.1662939
Coefficient of variation (CV)-0.3936261912
Kurtosis3.588197446
Mean-871.8075716
Median Absolute Deviation (MAD)0
Skewness2.348137519
Sum-42510209
Variance117763.1053
MonotonicityNot monotonic
2022-09-29T11:53:21.242037image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-99942841
87.9%
03060
 
6.3%
60296
 
0.6%
30235
 
0.5%
100204
 
0.4%
90200
 
0.4%
40197
 
0.4%
50165
 
0.3%
75158
 
0.3%
45147
 
0.3%
Other values (53)1258
 
2.6%
ValueCountFrequency (%)
-99942841
87.9%
03060
 
6.3%
105
 
< 0.1%
157
 
< 0.1%
20106
 
0.2%
2550
 
0.1%
30235
 
0.5%
3515
 
< 0.1%
40197
 
0.4%
45147
 
0.3%
ValueCountFrequency (%)
6401
 
< 0.1%
5401
 
< 0.1%
5001
 
< 0.1%
48015
< 0.1%
4502
 
< 0.1%
4201
 
< 0.1%
40011
< 0.1%
3902
 
< 0.1%
3757
< 0.1%
36012
< 0.1%

Low Wind NW
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct62
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-870.5324747
Minimum-999
Maximum530
Zeros2623
Zeros (%)5.4%
Negative42841
Negative (%)87.9%
Memory size381.1 KiB
2022-09-29T11:53:21.318940image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-999
5-th percentile-999
Q1-999
median-999
Q3-999
95-th percentile50
Maximum530
Range1529
Interquartile range (IQR)0

Descriptive statistics

Standard deviation346.649551
Coefficient of variation (CV)-0.398204043
Kurtosis3.590924965
Mean-870.5324747
Median Absolute Deviation (MAD)0
Skewness2.348858509
Sum-42448034
Variance120165.9112
MonotonicityNot monotonic
2022-09-29T11:53:21.393369image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-99942841
87.9%
02623
 
5.4%
60324
 
0.7%
40259
 
0.5%
90237
 
0.5%
75214
 
0.4%
30201
 
0.4%
120198
 
0.4%
100194
 
0.4%
150192
 
0.4%
Other values (52)1478
 
3.0%
ValueCountFrequency (%)
-99942841
87.9%
02623
 
5.4%
104
 
< 0.1%
159
 
< 0.1%
2044
 
0.1%
2540
 
0.1%
30201
 
0.4%
3526
 
0.1%
40259
 
0.5%
4594
 
0.2%
ValueCountFrequency (%)
5301
 
< 0.1%
5001
 
< 0.1%
4902
 
< 0.1%
48018
< 0.1%
4301
 
< 0.1%
4209
< 0.1%
4101
 
< 0.1%
4006
 
< 0.1%
3801
 
< 0.1%
36018
< 0.1%

Moderate Wind NE
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct39
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-874.7220935
Minimum-999
Maximum360
Zeros3762
Zeros (%)7.7%
Negative42841
Negative (%)87.9%
Memory size381.1 KiB
2022-09-29T11:53:21.470975image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-999
5-th percentile-999
Q1-999
median-999
Q3-999
95-th percentile0
Maximum360
Range1359
Interquartile range (IQR)0

Descriptive statistics

Standard deviation334.6377299
Coefficient of variation (CV)-0.3825646253
Kurtosis3.438487824
Mean-874.7220935
Median Absolute Deviation (MAD)0
Skewness2.327555597
Sum-42652324
Variance111982.4103
MonotonicityNot monotonic
2022-09-29T11:53:21.546141image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
-99942841
87.9%
03762
 
7.7%
60258
 
0.5%
50232
 
0.5%
30231
 
0.5%
40210
 
0.4%
90168
 
0.3%
100156
 
0.3%
20135
 
0.3%
7599
 
0.2%
Other values (29)669
 
1.4%
ValueCountFrequency (%)
-99942841
87.9%
03762
 
7.7%
54
 
< 0.1%
106
 
< 0.1%
1534
 
0.1%
20135
 
0.3%
2587
 
0.2%
30231
 
0.5%
3521
 
< 0.1%
40210
 
0.4%
ValueCountFrequency (%)
3602
 
< 0.1%
3201
 
< 0.1%
3006
 
< 0.1%
2704
 
< 0.1%
2402
 
< 0.1%
2208
< 0.1%
2101
 
< 0.1%
20016
< 0.1%
18014
< 0.1%
17513
< 0.1%

Moderate Wind SE
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct40
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-874.9173315
Minimum-999
Maximum300
Zeros3927
Zeros (%)8.1%
Negative42841
Negative (%)87.9%
Memory size381.1 KiB
2022-09-29T11:53:21.620544image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-999
5-th percentile-999
Q1-999
median-999
Q3-999
95-th percentile0
Maximum300
Range1299
Interquartile range (IQR)0

Descriptive statistics

Standard deviation334.1195195
Coefficient of variation (CV)-0.3818869595
Kurtosis3.44046976
Mean-874.9173315
Median Absolute Deviation (MAD)0
Skewness2.327816745
Sum-42661844
Variance111635.8533
MonotonicityNot monotonic
2022-09-29T11:53:21.694908image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
-99942841
87.9%
03927
 
8.1%
30251
 
0.5%
50201
 
0.4%
60196
 
0.4%
40177
 
0.4%
20141
 
0.3%
90118
 
0.2%
100108
 
0.2%
75101
 
0.2%
Other values (30)700
 
1.4%
ValueCountFrequency (%)
-99942841
87.9%
03927
 
8.1%
54
 
< 0.1%
1010
 
< 0.1%
1541
 
0.1%
20141
 
0.3%
2562
 
0.1%
30251
 
0.5%
3533
 
0.1%
40177
 
0.4%
ValueCountFrequency (%)
3005
 
< 0.1%
2704
 
< 0.1%
25012
< 0.1%
2406
 
< 0.1%
2251
 
< 0.1%
2106
 
< 0.1%
2007
 
< 0.1%
18013
< 0.1%
17523
< 0.1%
17010
< 0.1%

Moderate Wind SW
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct38
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-875.8405078
Minimum-999
Maximum330
Zeros4261
Zeros (%)8.7%
Negative42841
Negative (%)87.9%
Memory size381.1 KiB
2022-09-29T11:53:21.767057image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-999
5-th percentile-999
Q1-999
median-999
Q3-999
95-th percentile0
Maximum330
Range1329
Interquartile range (IQR)0

Descriptive statistics

Standard deviation331.5039256
Coefficient of variation (CV)-0.3784980515
Kurtosis3.413807682
Mean-875.8405078
Median Absolute Deviation (MAD)0
Skewness2.323989513
Sum-42706859
Variance109894.8527
MonotonicityNot monotonic
2022-09-29T11:53:21.840865image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
-99942841
87.9%
04261
 
8.7%
60238
 
0.5%
20225
 
0.5%
30223
 
0.5%
40191
 
0.4%
50134
 
0.3%
2582
 
0.2%
7572
 
0.1%
8068
 
0.1%
Other values (28)426
 
0.9%
ValueCountFrequency (%)
-99942841
87.9%
04261
 
8.7%
1026
 
0.1%
1541
 
0.1%
20225
 
0.5%
2582
 
0.2%
30223
 
0.5%
3531
 
0.1%
40191
 
0.4%
4529
 
0.1%
ValueCountFrequency (%)
3301
 
< 0.1%
2801
 
< 0.1%
2601
 
< 0.1%
2404
 
< 0.1%
2204
 
< 0.1%
2007
< 0.1%
1809
< 0.1%
1757
< 0.1%
17010
< 0.1%
1608
< 0.1%

Moderate Wind NW
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct37
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-875.4796661
Minimum-999
Maximum360
Zeros4095
Zeros (%)8.4%
Negative42841
Negative (%)87.9%
Memory size381.1 KiB
2022-09-29T11:53:21.912019image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-999
5-th percentile-999
Q1-999
median-999
Q3-999
95-th percentile0
Maximum360
Range1359
Interquartile range (IQR)0

Descriptive statistics

Standard deviation332.514782
Coefficient of variation (CV)-0.3798086864
Kurtosis3.421993844
Mean-875.4796661
Median Absolute Deviation (MAD)0
Skewness2.325159357
Sum-42689264
Variance110566.0802
MonotonicityNot monotonic
2022-09-29T11:53:21.985608image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
-99942841
87.9%
04095
 
8.4%
30277
 
0.6%
40238
 
0.5%
50190
 
0.4%
60164
 
0.3%
20149
 
0.3%
90103
 
0.2%
100102
 
0.2%
7595
 
0.2%
Other values (27)507
 
1.0%
ValueCountFrequency (%)
-99942841
87.9%
04095
 
8.4%
1010
 
< 0.1%
1532
 
0.1%
20149
 
0.3%
2561
 
0.1%
30277
 
0.6%
3531
 
0.1%
40238
 
0.5%
4550
 
0.1%
ValueCountFrequency (%)
3601
 
< 0.1%
3004
 
< 0.1%
2601
 
< 0.1%
2409
< 0.1%
2104
 
< 0.1%
20014
< 0.1%
18018
< 0.1%
1753
 
< 0.1%
1609
< 0.1%
15014
< 0.1%

High Wind NE
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-876.7290252
Minimum-999
Maximum180
Zeros4731
Zeros (%)9.7%
Negative42841
Negative (%)87.9%
Memory size381.1 KiB
2022-09-29T11:53:22.053107image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-999
5-th percentile-999
Q1-999
median-999
Q3-999
95-th percentile0
Maximum180
Range1179
Interquartile range (IQR)0

Descriptive statistics

Standard deviation328.9971624
Coefficient of variation (CV)-0.3752552418
Kurtosis3.389716706
Mean-876.7290252
Median Absolute Deviation (MAD)0
Skewness2.320543186
Sum-42750184
Variance108239.1329
MonotonicityNot monotonic
2022-09-29T11:53:22.115097image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
-99942841
87.9%
04731
 
9.7%
20200
 
0.4%
30154
 
0.3%
25132
 
0.3%
15109
 
0.2%
40100
 
0.2%
5099
 
0.2%
6087
 
0.2%
7568
 
0.1%
Other values (14)240
 
0.5%
ValueCountFrequency (%)
-99942841
87.9%
04731
 
9.7%
1027
 
0.1%
15109
 
0.2%
20200
 
0.4%
25132
 
0.3%
30154
 
0.3%
3543
 
0.1%
40100
 
0.2%
4541
 
0.1%
ValueCountFrequency (%)
1809
 
< 0.1%
1206
 
< 0.1%
1103
 
< 0.1%
1053
 
< 0.1%
10010
 
< 0.1%
9026
 
0.1%
855
 
< 0.1%
8023
 
< 0.1%
7568
0.1%
7032
0.1%

High Wind SE
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct21
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-876.8198765
Minimum-999
Maximum250
Zeros4767
Zeros (%)9.8%
Negative42841
Negative (%)87.9%
Memory size381.1 KiB
2022-09-29T11:53:22.178384image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-999
5-th percentile-999
Q1-999
median-999
Q3-999
95-th percentile0
Maximum250
Range1249
Interquartile range (IQR)0

Descriptive statistics

Standard deviation328.7452997
Coefficient of variation (CV)-0.3749291142
Kurtosis3.388404973
Mean-876.8198765
Median Absolute Deviation (MAD)0
Skewness2.320341502
Sum-42754614
Variance108073.472
MonotonicityNot monotonic
2022-09-29T11:53:22.241505image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
-99942841
87.9%
04767
 
9.8%
20210
 
0.4%
30165
 
0.3%
15127
 
0.3%
2593
 
0.2%
6093
 
0.2%
4089
 
0.2%
5067
 
0.1%
7557
 
0.1%
Other values (11)252
 
0.5%
ValueCountFrequency (%)
-99942841
87.9%
04767
 
9.8%
1053
 
0.1%
15127
 
0.3%
20210
 
0.4%
2593
 
0.2%
30165
 
0.3%
3549
 
0.1%
4089
 
0.2%
4550
 
0.1%
ValueCountFrequency (%)
2505
 
< 0.1%
10014
 
< 0.1%
9027
 
0.1%
854
 
< 0.1%
8011
 
< 0.1%
7557
0.1%
7030
 
0.1%
651
 
< 0.1%
6093
0.2%
558
 
< 0.1%

High Wind SW
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct20
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-877.0902771
Minimum-999
Maximum150
Zeros4929
Zeros (%)10.1%
Negative42841
Negative (%)87.9%
Memory size381.1 KiB
2022-09-29T11:53:22.303702image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-999
5-th percentile-999
Q1-999
median-999
Q3-999
95-th percentile0
Maximum150
Range1149
Interquartile range (IQR)0

Descriptive statistics

Standard deviation327.9895478
Coefficient of variation (CV)-0.3739518684
Kurtosis3.382538499
Mean-877.0902771
Median Absolute Deviation (MAD)0
Skewness2.319499617
Sum-42767799
Variance107577.1435
MonotonicityNot monotonic
2022-09-29T11:53:22.366590image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
-99942841
87.9%
04929
 
10.1%
30188
 
0.4%
20179
 
0.4%
15153
 
0.3%
1092
 
0.2%
2574
 
0.2%
4074
 
0.2%
4559
 
0.1%
6050
 
0.1%
Other values (10)122
 
0.3%
ValueCountFrequency (%)
-99942841
87.9%
04929
 
10.1%
54
 
< 0.1%
1092
 
0.2%
15153
 
0.3%
20179
 
0.4%
2574
 
0.2%
30188
 
0.4%
3525
 
0.1%
4074
 
0.2%
ValueCountFrequency (%)
1507
 
< 0.1%
1204
 
< 0.1%
1002
 
< 0.1%
906
 
< 0.1%
7517
 
< 0.1%
708
 
< 0.1%
6050
0.1%
552
 
< 0.1%
5047
0.1%
4559
0.1%

High Wind NW
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct21
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-876.9525645
Minimum-999
Maximum180
Zeros4890
Zeros (%)10.0%
Negative42841
Negative (%)87.9%
Memory size381.1 KiB
2022-09-29T11:53:22.428221image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-999
5-th percentile-999
Q1-999
median-999
Q3-999
95-th percentile0
Maximum180
Range1179
Interquartile range (IQR)0

Descriptive statistics

Standard deviation328.3761783
Coefficient of variation (CV)-0.3744514716
Kurtosis3.385838502
Mean-876.9525645
Median Absolute Deviation (MAD)0
Skewness2.319976412
Sum-42761084
Variance107830.9145
MonotonicityNot monotonic
2022-09-29T11:53:22.489389image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
-99942841
87.9%
04890
 
10.0%
20187
 
0.4%
30145
 
0.3%
15134
 
0.3%
5092
 
0.2%
2588
 
0.2%
4076
 
0.2%
6070
 
0.1%
7568
 
0.1%
Other values (11)170
 
0.3%
ValueCountFrequency (%)
-99942841
87.9%
04890
 
10.0%
1053
 
0.1%
15134
 
0.3%
20187
 
0.4%
2588
 
0.2%
30145
 
0.3%
3536
 
0.1%
4076
 
0.2%
4538
 
0.1%
ValueCountFrequency (%)
1808
 
< 0.1%
1205
 
< 0.1%
1002
 
< 0.1%
9011
 
< 0.1%
804
 
< 0.1%
7568
0.1%
704
 
< 0.1%
651
 
< 0.1%
6070
0.1%
558
 
< 0.1%

Category
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size48.0 KiB
Category 1
38672 
Category 2
7068 
Category 3
 
1933
Category 4
 
791
Category 5
 
297

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters487610
Distinct characters14
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCategory 2
2nd rowCategory 2
3rd rowCategory 2
4th rowCategory 2
5th rowCategory 2

Common Values

ValueCountFrequency (%)
Category 138672
79.3%
Category 27068
 
14.5%
Category 31933
 
4.0%
Category 4791
 
1.6%
Category 5297
 
0.6%

Length

2022-09-29T11:53:22.551964image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-29T11:53:22.612953image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
category48761
50.0%
138672
39.7%
27068
 
7.2%
31933
 
2.0%
4791
 
0.8%
5297
 
0.3%

Most occurring characters

ValueCountFrequency (%)
C48761
10.0%
a48761
10.0%
t48761
10.0%
e48761
10.0%
g48761
10.0%
o48761
10.0%
r48761
10.0%
y48761
10.0%
48761
10.0%
138672
7.9%
Other values (4)10089
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter341327
70.0%
Uppercase Letter48761
 
10.0%
Space Separator48761
 
10.0%
Decimal Number48761
 
10.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a48761
14.3%
t48761
14.3%
e48761
14.3%
g48761
14.3%
o48761
14.3%
r48761
14.3%
y48761
14.3%
Decimal Number
ValueCountFrequency (%)
138672
79.3%
27068
 
14.5%
31933
 
4.0%
4791
 
1.6%
5297
 
0.6%
Uppercase Letter
ValueCountFrequency (%)
C48761
100.0%
Space Separator
ValueCountFrequency (%)
48761
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin390088
80.0%
Common97522
 
20.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
C48761
12.5%
a48761
12.5%
t48761
12.5%
e48761
12.5%
g48761
12.5%
o48761
12.5%
r48761
12.5%
y48761
12.5%
Common
ValueCountFrequency (%)
48761
50.0%
138672
39.7%
27068
 
7.2%
31933
 
2.0%
4791
 
0.8%
5297
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII487610
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C48761
10.0%
a48761
10.0%
t48761
10.0%
e48761
10.0%
g48761
10.0%
o48761
10.0%
r48761
10.0%
y48761
10.0%
48761
10.0%
138672
7.9%
Other values (4)10089
 
2.1%

Interactions

2022-09-29T11:53:17.654445image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-29T11:52:58.084143image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-29T11:52:59.342401image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-29T11:53:00.533485image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-29T11:53:01.722625image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-29T11:53:02.920096image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-29T11:53:04.226085image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-29T11:53:05.403619image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-29T11:53:06.714354image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-29T11:53:07.881283image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-29T11:53:09.095168image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-29T11:53:10.431474image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-29T11:53:11.602192image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-29T11:53:12.777437image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-29T11:53:13.973678image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-29T11:53:15.310795image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-29T11:53:16.487983image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-29T11:53:17.720266image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-29T11:52:58.158760image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-29T11:52:59.409267image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-29T11:53:00.596687image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-29T11:53:01.793438image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-29T11:53:02.987839image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-29T11:53:04.292970image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-29T11:53:05.472192image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-29T11:53:06.778578image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-29T11:53:07.951419image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-29T11:53:09.162714image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-29T11:53:10.497414image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-29T11:53:11.668664image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-29T11:53:12.844395image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-29T11:53:14.038659image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-29T11:53:15.377563image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-29T11:53:16.553337image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-29T11:53:17.789207image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-29T11:52:58.227486image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-29T11:52:59.478821image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-29T11:53:00.662874image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-29T11:53:01.864170image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-29T11:53:03.058899image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-29T11:53:04.363462image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-29T11:53:05.542563image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-09-29T11:53:04.017294image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-29T11:53:05.200128image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-29T11:53:06.504803image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-29T11:53:07.677911image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-29T11:53:08.884744image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-29T11:53:10.221556image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-29T11:53:11.395013image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-29T11:53:12.572785image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-29T11:53:13.766105image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-29T11:53:15.105704image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-29T11:53:16.282407image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-29T11:53:17.451164image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-29T11:53:18.689258image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-29T11:52:59.206807image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-29T11:53:00.395907image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-29T11:53:01.594325image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-29T11:53:02.783545image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-29T11:53:04.087247image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-29T11:53:05.267689image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-29T11:53:06.574926image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-29T11:53:07.745911image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-29T11:53:08.955349image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-29T11:53:10.290927image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-29T11:53:11.463881image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-29T11:53:12.642078image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-29T11:53:13.837062image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-29T11:53:15.173643image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-29T11:53:16.349802image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-29T11:53:17.519287image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-29T11:53:18.754999image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-29T11:52:59.275518image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-29T11:53:00.464534image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-29T11:53:01.658474image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-29T11:53:02.852361image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-29T11:53:04.156439image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-29T11:53:05.335934image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-29T11:53:06.644747image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-29T11:53:07.814306image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-29T11:53:09.025922image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-29T11:53:10.360115image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-29T11:53:11.533618image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-29T11:53:12.710223image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-29T11:53:13.905597image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-29T11:53:15.241006image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-29T11:53:16.417925image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-29T11:53:17.586290image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2022-09-29T11:53:22.677039image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-09-29T11:53:22.795408image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-09-29T11:53:22.914231image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-09-29T11:53:23.013654image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-09-29T11:53:23.080812image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-09-29T11:53:18.900124image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-09-29T11:53:19.226578image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexIDNameDateTimeEventStatusLatitudeLongitudeMaximum WindMinimum PressureLow Wind NELow Wind SELow Wind SWLow Wind NWModerate Wind NEModerate Wind SEModerate Wind SWModerate Wind NWHigh Wind NEHigh Wind SEHigh Wind SWHigh Wind NWCategory
00AL011851UNNAMED185106250HU28.0N94.8W80-999-999-999-999-999-999-999-999-999-999-999-999-999Category 2
11AL011851UNNAMED18510625600HU28.0N95.4W80-999-999-999-999-999-999-999-999-999-999-999-999-999Category 2
22AL011851UNNAMED185106251200HU28.0N96.0W80-999-999-999-999-999-999-999-999-999-999-999-999-999Category 2
33AL011851UNNAMED185106251800HU28.1N96.5W80-999-999-999-999-999-999-999-999-999-999-999-999-999Category 2
44AL011851UNNAMED185106252100LHU28.2N96.8W80-999-999-999-999-999-999-999-999-999-999-999-999-999Category 2
55AL011851UNNAMED185106260HU28.2N97.0W70-999-999-999-999-999-999-999-999-999-999-999-999-999Category 1
66AL011851UNNAMED18510626600TS28.3N97.6W60-999-999-999-999-999-999-999-999-999-999-999-999-999Category 1
77AL011851UNNAMED185106261200TS28.4N98.3W60-999-999-999-999-999-999-999-999-999-999-999-999-999Category 1
88AL011851UNNAMED185106261800TS28.6N98.9W50-999-999-999-999-999-999-999-999-999-999-999-999-999Category 1
99AL011851UNNAMED185106270TS29.0N99.4W50-999-999-999-999-999-999-999-999-999-999-999-999-999Category 1

Last rows

df_indexIDNameDateTimeEventStatusLatitudeLongitudeMaximum WindMinimum PressureLow Wind NELow Wind SELow Wind SWLow Wind NWModerate Wind NEModerate Wind SEModerate Wind SWModerate Wind NWHigh Wind NEHigh Wind SEHigh Wind SWHigh Wind NWCategory
4875149095AL122015KATE20151111600HU35.2N67.6W70985801001002030600002000Category 1
4875249096AL122015KATE201511111200HU36.2N62.5W7598012018018050609060003000Category 2
4875349097AL122015KATE201511111800HU37.6N58.2W6598015020018060609060004000Category 1
4875449098AL122015KATE201511120EX38.9N55.0W65980180210180909011060004000Category 1
4875549099AL122015KATE20151112600EX40.0N52.0W6598022022018012012012060004000Category 1
4875649100AL122015KATE201511121200EX41.3N50.4W559812202201801201201206000000Category 1
4875749101AL122015KATE201511121800EX41.9N49.9W559832202201801201201206000000Category 1
4875849102AL122015KATE201511130EX41.5N49.2W509855405202002201201206000000Category 1
4875949103AL122015KATE20151113600EX40.8N47.5W4598562046018022000000000Category 1
4876049104AL122015KATE201511131200EX40.7N45.4W4598771040015022000000000Category 1